Model of knowledge transfer in the processes of mergers and acquisitions

The ambiguity of the notion of model derives from the fact that this notion occurs in various scientific disciplines.

By „model” generally a simplified representation of a complex object is understood¹²¹. Construction of a model may strive to know the existing, complex state of things, i.e. structure, functioning and development. For modelling results to be scientifically valid, it must be verified by simulation¹²².

The notion of a model can be understood as a copy of a complex system that we intend to study¹²³. Among the reasons justifying the need to create models are¹²⁴:

• focusing on important features of the system, omitting the less relevant ones,

• introducing changes and corrections that are adequate to requirements of the user (low cost and minimal risk),

• verifying that the user environment is understood and documented in a manner allowing the designers and programmers to build the system.

We distinguish many different types of systems. It can be assumed that everything we encounter in everyday life is the system or its component. According to Webster’s New Collegiate Dictionary it is:

• a group of interacting, interrelated, or interdependent elements forming a complex whole,

121 A. Groble, Metodologia nauk, Areus, Znak, Kraków 2006, p. 175.

122 S. Sudoł, Badania naukowe w zakresie zarządzania, in: Dynamika zarządzania organizacjami. Paradygmaty – Metody – Zastosowania. Księga pamiątkowa wydana z okazji 50-lecia pracy naukowej prof. zw. dr. hab. J. Rokity, Prace Naukowe Akademii Ekonomicznej im.

Karola Adamieckiego w Katowicach, Katowice 2007, p. 373–374.

123 L.J. Krzyżanowski, O podstawach kierowania organizacjami inaczej. Paradygmaty, metafory, modele. Filozofia, metodologia. Dylematy, trendy, PWN, Warszawa 1999, p. 28–45.

124 E. Yourdan, Współczesna analiza strukturalna, WNT, Warszawa 1996, p. 120.

• an organized set of doctrines, ideas or rules designed to explain the construction or operation of a certain systematic whole,

• harmonious interaction or order,

• organized society or social situation treated as sustainable organization¹²⁵. Increase in complexity of the designed systems results in increased demands for the designed systems. New design techniques are being sought to shorten the design cycle and achieve the highest quality design solutions. One of the key capabilities in this area is the extensive use of modelling technique. The essence of modelling is to present the original in a simplified manner.

The original is understood as a slice of reality in terms of existing or future real physical objects or processes. Model is an abstract design, representation of the original, obtained by omitting its insignificant properties that are not of interest in this dissertation. The model, i.e. a substitutionary form of the original, is less complex than the presented reality, and therefore easier to use for research or design purposes. The model is a quantitative, qualitative or quantitative-qualitative representation of the original that allows mapping, understanding and exploring the essential features and relations between the factors that were considered. It is a compromise between the desire to faithfully represent the studied part of reality (in the scope of including the largest possible number of factors) and the possibility of its reflection (the more factors the model considers, the more difficult it is to build the model and its study and inference).

It can be observed that the simpler the model is, the more abstraction it contains.

The closer a model comes to reality, the more impact factors will occur, and the more difficult it will be to master such a model. Therefore, to create a model it is necessary to adopt simplistic assumptions and constraints that will always be the reason for provoking discussions about relations between the model and the reality.

The sense of modelling consists in the fact that the model is more convenient for research than the original, without incurring excessive costs. For modelling, two issues are of crucial meaning:

• purpose for which the model is created,

• mutual correlations between model features and original features.

By building a model we overlook certain features, leaving others. The aim of abstraction, as the most important element of modelling, is to separate the non-essential features (due to the model’s purpose) from the relevant ones, i.e. the ones that are the subject of interest and subject to research. The degree of simplification of the original features for needs of the model is influenced by the correlation between individual properties of the original. One cannot allow here to reject the feature

125 G. & C. Meriam, Webster’s New Collegiate Dictionary, Mass Company, Springfield 1977.

strongly correlated with the attributes of great importance (in this paper), as this would lead to an incorrect model.

Modelling is fundamentally based on the principle of isomorphism, i.e. mutual equality of physically diverse phenomena. This allows to reproduce or express real phenomena and objects, using isomorphic models, which differ from their original in physical characteristics. The isomorphic model is more suited to testing than the original. In constructing the model, apart from the isomorphism principle the principle of analogy is applied¹²⁶. Analogy is a kind of similarity of phenomena. It is used in all areas of human activity, including design. When analysing a complex design problem for component problems, the similarity between them and problems already solved or analogies to other problem classes is often observed.

Models are characterized by some characteristic traits that embody their essence:

• hypothetical nature – the model is a „supposition” that the original shown in simplified manner represents it well;

• subjectivism – the model is a reproduction of the original in a degree determined by the needs;

• relative simplicity – the model is a simplification that seeks to limit the number of values occurring in it and correlate them, or to limit the form of the dependencies;

• diversity – different models of the same original for different purposes coexist; this is even necessary as it allows the original to be reproduced from different points of view.

Based on the study of literature of the subject matter, the basic characteristics of knowledge transfer between the merging companies have been identified. Very often, models are used to analyse various phenomena. In the modelling process we also use the language of mathematics and logic¹²⁷. The characteristics of the model do not coincide, however, with characteristics of the described phenomenon, in this case transfer of knowledge. The model contains less of them than in the process described by them. This is a necessary simplification, since the possible inclusion of the model to the due-diligence analysis should not lead to its excessive complication. Obtaining data to use a very advanced model would be impossible in practice. Besides, incorporating the principle of the universe of phenomena in the model prevents it from being built. By constructing a model, we leave out all variables in it, limiting ourselves to the most important ones¹²⁸.

126 L.J. Krzyżanowski, O podstawach kierowania…, op. cit., p. 37.

127 T. Trzaskalik, Modelowanie optymalizacyjne, Absolwent, Łódź 2001, p. 5.

128 S. Bartosiewicz, Modele ekonometryczne – kwalifikacja zmiennych występujących w modelu, in: Z. Hellwig (ed.), Zarys ekonometrii, PWE, Warszawa 1970, p. 13.

Contemporary social sciences, such as knowledge management, use mathematics, and usually form the norms in society in the following manner: the value of the phenomenon X is a function of the values of the phenomena V, W, Y, Z ...¹²⁹. This results in the need for granting an analytical character, which is primarily comes down to construction of the model and estimation of its parameters.

The main purpose of developing a research model is to calculate the total time of knowledge transfer in the planned process of businesses’ consolidation as part of their merger or acquisition.

Firstly, function of the target or function-criterion is set. The purpose of knowledge transfer is to gain knowledge from the acquired company. The advantage may also include the transfer of own knowledge to improve the condition of the new, merged company and increase its market value. Delay in the transfer of knowledge, as part of enterprise integration, results in loss of benefit. Patents, innovations, management and crew skills as well as organizational knowledge transferred too late often result in loss of benefit, for example from the planned synergy.

Therefore, the measure of the transfer success is time. In each unit of time, the company that acquired the other company gains a certain substantial advantage. The delay in transfer also causes a countable loss. Therefore, as a measurable variable, constituting a function-criterion, the total time of knowledge transfer should be assumed. The shorter the time, the greater the benefit from application of the acquired knowledge will be. If we denote this time as Y_n, then we should strive for this to be as small as possible, i.e. Y → minimum. The next step in the analysis is selection of variables that shape the value Y_n. These variables are the amount of knowledge transferred. It can be expressed by averaged times, necessary to convey it.

The knowledge transfer should not, of course, be understood mechanically as covering a certain distance from one business to another. Transfer is understood as mastering (learning) knowledge, understood as skills, relations, powers or experience.

Such transfer is not possible immediately and it must take a certain time, especially with regard to tacit knowledge. This period, measured in months or seldom in years, can be a measure of the knowledge transfer effectiveness.

The fact that the model explains several variables mentioned above, and what the variables are requires explanation. While striving to convey knowledge as a whole, however, (as already mentioned) there are different kinds of knowledge with different degrees of perception. This causes – depending on whether it is tacit or explicit knowledge, whether it is more or less complicated, whether it is provided easily or difficult, willingly or reluctantly, etc., the transfer times to differ significantly.

129 Ibidem, p. 56.

Therefore, in the first equation of the model four variables x_n (x₁ x₂ x₃ x₄) are provided. Each of them represents another type of knowledge, interpreted as the time necessary to master it, counted in the months of transfer. These times may take different values due to circumstances, such as resignation from the transfer of certain kind of knowledge or vice versa – because of finding additional sources of knowledge. They can also be used for experimental calculations, namely to answer the question of how long it will take to wait, for example, for a particular technology to be acquired if the company is planning to acquire a particular technology.

When forming knowledge into larger groups, introduction of the following variables is proposed¹³⁰:

x₁ – knowledge that is an individual motive for acquisition (patents, inventions, important technologies, etc.);

x₂ – knowledge, including tacit knowledge that is relevant to the acquiring entity (e.g. specific managerial competencies, unique contractor skills, etc.);

x₃ – knowledge, including explicit knowledge, of relevance (relations, experience, etc.);

x₄ – organisational knowledge characteristic of certain enterprises (pay system, regulations, protocols, important legal documents, etc.).

The variables described above were grouped on the basis of conclusions drawn from the previous chapters, discussing the division of knowledge transferred into types and categories.

Variables x_n define the „mass” of knowledge to transfer, but do not indicate its meaning, which varies according to the type of knowledge. It is necessary to have coefficients with constant character, which can differentiate knowledge transferred on account of its significance.

These coefficients will be identified by the symbols A, B, C, D.

Expert qualifications¹³¹ have allowed to propose to companies operating in the metallurgical industry the coefficients assigned to particular types of knowledge.

The method of expert consultation aimed at gathering opinions that served to formulate a position concerning the importance of knowledge significance due to the motives behind its transfer.

Expert consultations took place through meetings with scientists from the AGH University of Science and Technology in Kraków and an institute specializing in

130 The procedure for identifying and dividing the knowledge factors, related to the set research goal was based on a critical analysis of the subject matter literature, the author’s experience and suggestions of the people directly related to the researched subject.

131 Z. Fend, Expert Consultation – Comprehensive Analysis Method on the 2-mode Network of Expert Consultation, Dept. of Autom., Tsinghua University, Beijing, China, BCGIN, Shanghai, October 2012.

analyses of the iron and steel market. Through expert and consultation workshops in the form of directional recommendations and suggestions, a in recommendation was formulated in the form of A, B, C and D coefficients.

They assume the following values: A = 4.0, B = 3.0, C = 1.5, D = 1.

Taking into account the above-mentioned coefficients (and their values: A = 4.0, B = 3.0, C = 1.5, D = 1.0) the diagram illustrating the impact of particular types of knowledge, taking into account their importance for knowledge transfer, is as follows.

Figure 25. Type of knowledge and its importance for transfer

S T A RT

Load x₁, x₂, x₃, x₄

Y₁ = Ax₁ + Bx₂ + Cx₃ + Dx₄

Y₁ ≥ Y₂

Y₁ ≤ Y₂

E N D

E N D

E N D

"total transfer time -minimum"

"total transfer time -maximum"

Source: own study.

The best situation would be when the Y value, that is, the total transfer time, would be the smallest, i.e. when Y → minimum.

The transfer operations described above are not sufficient, as they do not cover a different situation where the acquiring enterprise not only collects the knowledge from the acquired company, but also gives it in order to increase the goodwill and gain additional benefits.

This is a transfer of knowledge in direction contrary to the previous one, as referred to in the second chapter. In this case, flow vectors of opposite directions will not neutralize each other, but they add. This is due to the fact that it is certainly not the same knowledge.

As a result of identification of the knowledge flow variables from the acquiring enterprise to the acquired company by x_n^m, where n = 1 ... 4 and m = 1 ... 4, and the total transfer time is Y, for n = 1 ... 4 and m = 1 .. 4, the equation shown in Figure 26 is obtained.

Figure 26. Knowledge transfer time

S T A RT

Readx1, x2, x3, x4

E N D

where n = 1...4 and m = 1...4, and the total transfer time equals to

Y for n = 1...4 and m = 1...4

Y₂=x₁¹+x₂²+x₃³+x₄⁴ x_n^m

Source: own study.

Superscript m = 1 means knowledge transfer from the acquiring entity to the acquired company, and after the coefficients are given it takes the form:

Y₂ =4x₁¹+3x₂²+1 5. x₃³+x₄⁴. (1) It is only the total time of knowledge transfer in both directions, that is, from the acquired to the acquiring and vice versa, that determines the final transfer time and transfer of knowledge. This is expressed by the sum of both equations.

Y₁+Y₂¹ =4⁽x₁+x₁¹⁾+3

(

x₂+x₂²

)

⁺1 5^.

(

x₃⁺x₃³

)

⁺⁽x₄⁺x₄^{4). (2)} Equations that constitute model being the basis for further reflection (Figure 27).

As with any other model, the following equation is limited by defined boundary and organizational conditions. The description of the variables shows that they must satisfy the weak inequality x_n ≥0 and x_n^m≥0.

In practice, knowledge cannot have a negative value; at most, it may not be useful, meaning zero.

Organizational conditions have other characters. Transfer taking too long, i.e.

mastering the knowledge of the other company, must be limited in time. 5 years is the maximum time during which knowledge transfer takes place. This limitation was adopted for variables: x_n ≤60 months and x_n^m ≤60 months.

Figure 27. Model of knowledge transfer in the processes of mergers and acquisitions

S T A RT

Verification of model operation was performed on fictitious data:

x₁ =6 ^months, x₂ =5 ^months, x₃ =2 months, x₄ =1 month, x₁¹ =0 ^months, x₂² =3 months, x₃³ =2 ^months, x₄⁴ =1 month.

After placing these values in the formulas, the following were obtained:

Y₁=24 15 3 1+ + + =43 months, Y₂ = + + + =0 9 3 1 13 months, where:

Y₁ – total transfer time for the acquiring enterprise Y₂ – total transfer time for the acquired enterprise.

This means that in the example given, the transfer of knowledge will take a total of 56 months. Vector of knowledge transfer is 13 months.

Each transfer requires action and application of appropriate measures: human, material and financial, which are limited. Assuming, however, that an enterprise is particularly keen on the accelerated transfer of certain type of knowledge, such as important technology, it can shift engineers and staff involved in organizing the transfer of other knowledge to work on mastering the new technology. This allows, for example, to shorten the time at transfer x₁ by 1 month, at the expense of increasing the transfer time of knowledge passed x₂¹ by this value. Then the new variables will take the following values:

x₁ =5 ^months, x₂ =5 ^months, x₃ =2 ^months, x₄ =1 ^month, x₁¹ =0 months, x₂² =4 months, x₃³ =2 months, x₄⁴ =1 month.

After placing new values of variable x₁' and x₂' the equations will take the following form:

Y₁= × + × +4 5 3 5 1 5 2 1 39. × + = months, Y₂¹ = × + × +4 0 3 4 1 5 2 1 1 16. × + × = , months, Y₂¹+ =Y₁ 39 16+ =45 months,

whereas

Y₁+Y₂ =43 13+ =56 ^months, therefore

Y₁+Y₂ Y₁¹ Y₂¹ 56 55 1

( )

⁻

(

⁺

)

^{= − = month.}

In this manner, the time for learning knowledge has been reduced by 1 month.

It brings benefits of transferring new knowledge in time shorter by 1 month. This is purely theoretical deliberation, but shows the benefits (or losses) resulting from shifts and concentration on the transfer of a particular type of knowledge.

When considering the opportunity to acquire valuable knowledge from two different consolidations, the speed of transfer and the benefits of choosing each one can be compared. The problem is obtaining relevant information, but if it can be achieved, for example, within in-depth due diligence analysis, it would make selection of a candidate for merger easier. This is the case where the purpose of the merger or acquisition is the transfer of knowledge, otherwise the results of the analysis through the presented model would only be of an auxiliary nature.

Im Dokument KNOWLEDGE TRANSFER in merger and acquisition processes in the metallurgical industry (Seite 106-115)